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Title: GitHub - ARASC/DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

Open Graph Title: GitHub - ARASC/DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

X Title: GitHub - ARASC/DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

Description: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. - ARASC/DeepCTR

Open Graph Description: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. - ARASC/DeepCTR

X Description: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. - ARASC/DeepCTR

Opengraph URL: https://github.com/ARASC/DeepCTR

X: @github

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https://github.com/shenweichen/DeepMatchhttps://github.com/shenweichen/DeepMatch
https://github.com/shenweichen/DeepCTR-Torchhttps://github.com/shenweichen/DeepCTR-Torch
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A Convolutional Click Prediction Modelhttp://ir.ia.ac.cn/bitstream/173211/12337/1/A%20Convolutional%20Click%20Prediction%20Model.pdf
Deep Learning over Multi-field Categorical Data: A Case Study on User Response Predictionhttps://arxiv.org/pdf/1601.02376.pdf
Product-based neural networks for user response predictionhttps://arxiv.org/pdf/1611.00144.pdf
Wide & Deep Learning for Recommender Systemshttps://arxiv.org/pdf/1606.07792.pdf
DeepFM: A Factorization-Machine based Neural Network for CTR Predictionhttp://www.ijcai.org/proceedings/2017/0239.pdf
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Predictionhttps://arxiv.org/abs/1704.05194
Deep & Cross Network for Ad Click Predictionshttps://arxiv.org/abs/1708.05123
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networkshttp://www.ijcai.org/proceedings/2017/435
Neural Factorization Machines for Sparse Predictive Analyticshttps://arxiv.org/pdf/1708.05027.pdf
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systemshttps://arxiv.org/pdf/1803.05170.pdf
Deep Interest Network for Click-Through Rate Predictionhttps://arxiv.org/pdf/1706.06978.pdf
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networkshttps://arxiv.org/abs/1810.11921
Deep Interest Evolution Network for Click-Through Rate Predictionhttps://arxiv.org/pdf/1809.03672.pdf
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertisinghttps://arxiv.org/pdf/1806.03514.pdf
Operation-aware Neural Networks for User Response Predictionhttps://arxiv.org/pdf/1904.12579.pdf
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction https://arxiv.org/pdf/1904.04447
Deep Session Interest Network for Click-Through Rate Prediction https://arxiv.org/abs/1905.06482
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Predictionhttps://arxiv.org/pdf/1905.09433.pdf
FLEN: Leveraging Field for Scalable CTR Predictionhttps://arxiv.org/pdf/1911.04690.pdf
Behavior sequence transformer for e-commerce recommendation in Alibabahttps://arxiv.org/pdf/1905.06874.pdf
An Input-aware Factorization Machine for Sparse Predictionhttps://www.ijcai.org/Proceedings/2019/0203.pdf
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systemshttps://arxiv.org/abs/2008.13535
A Dual Input-aware Factorization Machine for CTR Predictionhttps://www.ijcai.org/Proceedings/2020/0434.pdf
Field-Embedded Factorization Machines for Click-through rate predictionhttps://arxiv.org/abs/2009.09931
An Overview of Multi-Task Learning in Deep Neural Networkshttps://arxiv.org/pdf/1706.05098.pdf
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Ratehttps://arxiv.org/abs/1804.07931
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Expertshttps://dl.acm.org/doi/abs/10.1145/3219819.3220007
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendationshttps://dl.acm.org/doi/10.1145/3383313.3412236
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